COM6513 Natural Language Processing
| Summary |
This module provides an introduction to the field of
computer processing of written natural language, known as
Natural Language Processing (NLP). We will cover standard
theories, models and algorithms, discuss competing
solutions to problems, describe example systems and
applications, and highlight areas of open research. Students should be aware that there are limited places available on this course. |
| Session |
Spring 2023/24 |
| Credits |
15 |
| Assessment |
- Assignment
- Formal examination
|
| Lecturer(s) |
Prof. Nikos Aletras |
| Resources |
|
| Aims |
- to give students a well-rounded feel for the problems
and approaches of Statistical Natural Language
Processing (NLP)
- to give students an understanding of the potential
areas of application of the techniques developed in
Statistical NLP
|
| Learning Outcomes |
By the end of this course the students should:
- be able to describe and discuss the subareas of NLP
- be able to implement NLP algorithms and
techniques;
- be able to describe and discuss the potential and
limitations of NLP techniques for applications such as
machine translation, question answering, information
retrieval and information extraction
|
| Content |
Lectures will provide an overview of the field of NLP and
its sub-areas, and will introduce and explain its key
techniques, including their applicability and limitations.
In lab classes, students will practice implementing the
NLP techniques taught in class, testing their code in
application to real language data. Topics covered will
include:
- N-gram Language Modelling
- Word Classes and Part-of-Speech Tagging
- Lexical Semantics and
Lexical Similarity
- Syntactic and semantic parsing
- Information extraction
- Neural network architectures for NLP
|
| Restrictions |
Students must have taken Text Processing (COM6115) and Machine Learning and Adaptive Intelligence (COM6509) in the previous semester. The maximum number of students allowed on the module is 160. |
| Teaching Method |
There will be 2 formal lectures and 1 lab session per
week. |
| Feedback |
Written feedback for the assignment.
Verbal interaction during lectures and lab sessions. |
|
